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import time
from scipy.io.wavfile import write, read
import numpy as np
# from typing import Union
# from pydantic import BaseModel
from fastapi import FastAPI, UploadFile
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import FileResponse
# from fastapi.staticfiles import StaticFiles
# from fastapi.responses import FileResponse
import torch
# from transformers import pipeline
from transformers import SeamlessM4Tv2Model
from transformers import AutoProcessor
model_name = "facebook/seamless-m4t-v2-large"
# model_name = "facebook/hf-seamless-m4t-medium"
processor = AutoProcessor.from_pretrained(model_name)
model = SeamlessM4Tv2Model.from_pretrained(model_name)
device = "cuda:0" if torch.cuda.is_available() else "cpu"
# torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
model.to(device)
app = FastAPI(docs_url="/api/docs")
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_methods=["*"],
allow_headers=["*"],
allow_credentials=True,
)
BATCH_SIZE = 8
@app.get("/device")
def getDevice():
start_time = time.time()
print("Time took to process the request and return response is {} sec".format(
time.time() - start_time))
return device
@app.get("/translate")
def transcribe(inputs, src_lang="eng", tgt_lang="por"):
start_time = time.time()
if inputs is None:
raise "No audio file submitted! Please upload or record an audio file before submitting your request."
text_inputs = processor(text=inputs,
src_lang=src_lang, return_tensors="pt").to(device)
output_tokens = model.generate(
**text_inputs, tgt_lang=tgt_lang, generate_speech=False)
translated_text_from_text = processor.decode(
output_tokens[0].tolist()[0], skip_special_tokens=True)
print("Time took to process the request and return response is {} sec".format(
time.time() - start_time))
return translated_text_from_text
@app.get("/audio")
async def audio(inputs, src_lang="eng", tgt_lang="por", speaker_id=5):
start_time = time.time()
if inputs is None:
raise "No audio file submitted! Please upload or record an audio file before submitting your request."
text_inputs = processor(text=inputs,
src_lang=src_lang, return_tensors="pt").to(device)
audio_array_from_text = model.generate(
**text_inputs, tgt_lang=tgt_lang, speaker_id=int(speaker_id))[0].cpu().numpy().squeeze()
write(f"/tmp/output{start_time}.wav", model.config.sampling_rate,
audio_array_from_text)
print("Time took to process the request and return response is {} sec".format(
time.time() - start_time))
return FileResponse(f"/tmp/output{start_time}.wav", media_type="audio/mpeg")
@app.post("/transcribe-audio")
async def transcribe_audio(soundFile: UploadFile, tgt_lang='eng'):
start_time = time.time()
with open(f"/tmp/{soundFile.filename}", "wb") as buffer:
buffer.write(soundFile.file.read())
sample_rate, audio_data = read(f"/tmp/{soundFile.filename}")
audio_inputs = processor(
audios=audio_data, return_tensors="pt").to(device)
audio_array_from_audio = model.generate(
**audio_inputs, tgt_lang=tgt_lang)[0].cpu().numpy().squeeze()
write(f"/tmp/output{start_time}.wav", model.config.sampling_rate,
audio_array_from_audio)
print("Time took to process the request and return response is {} sec".format(
time.time() - start_time))
return FileResponse(f"/tmp/output{start_time}.wav", media_type="audio/wav")
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